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How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool

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  • Kevin McNally

    (HSE Science and Research Centre, Health and Safety Executive, Buxton SK17 9JN, UK)

Abstract

The Advanced REACH Tool (ART) is the most detailed exposure model currently available for estimating inhalation exposures to dusts, vapours, and aerosols under a broad range of exposure scenarios. The ART follows a Bayesian approach, making use of a calibrated source–receptor model to provide central estimates of exposures and information on exposure variability from meta-analyses in the literature. Uniquely amongst exposure models, the ART provides a facility to update the baseline estimates from the mechanistic model and variance components using measurement data collected on the exposure scenario; however, in practical use, this facility is little used. In this paper, the full capability of the ART tool is demonstrated using a small number of carefully chosen case studies that each had a sufficient breadth of personal exposure measurement data to support a measurement-led exposure assessment. In total, six cases studies are documented, three where the estimate from the source–receptor model of the ART was consistent with measurement data, and a further three case studies where the source–receptor model of the ART was inconsistent with measurement data, resulting in a prior-data conflict. A simulation study was designed that involved drawing subsets of between two and ten measurements from the available measurement dataset, with estimates of the geometric mean (GM) and 90th percentile of exposures from the posterior distribution of ART compared against measurement-based estimates of these summaries. Results from this work indicate that very substantial reductions in the uncertainty associated with estimates of the GM and 90th percentile could be achieved with as few as two measurements, with results in detail sensitive to both the measurements themselves and worker and company labels associated with the measurements. For case studies involving prior-data conflicts, the estimates of the GM and 90th percentile rapidly changed as measurement data were used to update the prior. However, results suggest that the current statistical model of the ART does not allow a complete resolution of a prior-data conflict.

Suggested Citation

  • Kevin McNally, 2023. "How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool," IJERPH, MDPI, vol. 20(7), pages 1-14, April.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:7:p:5386-:d:1115391
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    References listed on IDEAS

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    1. S. T. Boris Choy & Adrian F. M. Smith, 1997. "On Robust Analysis of a Normal Location Parameter," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 463-474.
    2. Andrea Spinazzè & Francesca Borghi & Davide Campagnolo & Sabrina Rovelli & Marta Keller & Giacomo Fanti & Andrea Cattaneo & Domenico Maria Cavallo, 2019. "How to Obtain a Reliable Estimate of Occupational Exposure? Review and Discussion of Models’ Reliability," IJERPH, MDPI, vol. 16(15), pages 1-29, August.
    3. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    4. John William Cherrie & Wouter Fransman & Gerardus Antonius Henrikus Heussen & Dorothea Koppisch & Keld Alstrup Jensen, 2020. "Exposure Models for REACH and Occupational Safety and Health Regulations," IJERPH, MDPI, vol. 17(2), pages 1-8, January.
    5. J. Andrade & Edward Omey, 2013. "Modelling conflicting information using subexponential distributions and related classes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 491-511, June.
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